Data Query Adjustment via Case Information Extraction
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Solution Overview
Problem
Existing data retrieval techniques in on-demand systems require significant user intervention for fine-tuning queries, making it inefficient and less effective in providing relevant results.
Innovation Solution
The method involves identifying a case within the system, extracting relevant information, and adjusting the data query using this information to filter and optimize query results, allowing for more precise and efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data querying techniques are used in on-demand systems, then data retrieval can be performed, but significant user intervention is required for fine-tuning queries which reduces efficiency
Solution Approach 1:
The system automatically extracts information from the case and uses it to adjust the data query without requiring user intervention. The case information serves itself to optimize the query parameters, eliminating the need for manual fine-tuning while maintaining high retrieval efficiency.
Solution Approach 2:
The system uses feedback from the case information to dynamically adjust the data query. By extracting relevant data from the case and using it to modify query parameters, the system creates a feedback loop that automatically optimizes query results based on the specific case context.
2Measurement precision
If traditional data querying is performed without case information, then queries can be executed, but the relevance and precision of query results deteriorate
Solution Approach 1:
The system performs preliminary extraction of information from the case before executing the data query. By preparing and storing relevant case information in advance, the system can automatically adjust query parameters to improve result relevance without adding significant complexity to the query mechanism itself.
Data Source
AI summary
In accordance with embodiments, there are provided mechanisms and methods for adjusting a data query. These mechanisms and methods for adjusting a data query can enable more relevant query results, increased efficiency and revenue, optimized customer interaction, etc.